Machine Learning Multiple Choice Questions with Answers 27

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, lasso regression, tuning parameter, penalty parameter

Machine learning Quiz Questions - Set 27

1. In Lasso regression, if the tuning parameter (lambda) increases ______ increases.

a) Variance

b) Bias

c) Both variance and bias

d) Neither variance nor bias

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Ans : (b)

Answer: (b) Bias The regularization (tuning or penalty) parameter (lambda) is an input to your model. Lambda is the tuning parameter that controls the bias-variance tradeoff and we estimate its best value via cross-validation. The regularization parameter reduces over-fitting, which reduces the variance of your estimated regression parameters; however, it does this at the expense of adding bias to your estimate. Increasing lambda results in less over-fitting but also greater bias. Large values of lambda pull weight parameters to zero leading to large bias. It leads to under-fitting.

 

2. In Lasso regression, if the tuning parameter (lambda) decreases ______ increases.

a) Variance

b) Bias

c) Both variance and bias

d) Neither variance nor bias

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Ans : (a)

Answer: (a) Variance Small values of λ allow model to become finely tuned to noise leading to large variance. It leads to over-fitting.

 

3. “Less important parameters goes close to zero when we increase the value of tuning parameters” in which of the following regressions?

a) Ridge

b) Lasso

c) Both ridge and lasso

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Ans : (b)

Answer: (b) Lasso With Lasso, when we increase the value of Lambda the most important parameters shrink a little bit and the less important parameters goes closed to zero. So, Lasso is able to exclude silly parameters from the model.

 

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List the type of regularized regression

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Machine Learning Multiple Choice Questions with Answers 26

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, overfitting in non-parametric machine learning algorithms, decision tree, lasso regression

Machine learning Quiz Questions - Set 26

 

1. What strategies can help reduce over-fitting in decision trees?

a) Pruning

b) Make sure each leaf node is one pure class

c) Enforce a maximum depth for the tree

d) Enforce a maximum number of samples in leaf nodes

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Ans : (a) and (c)

Answer: (a) Pruning and (c) Enforce a maximum depth for the tree Over-fitting is a significant practical difficulty for decision tree models and many other predictive models. Over-fitting happens when the learning algorithm continues to develop hypotheses that reduce training set error at the cost of an increased test set error. Unlike other regression models, decision tree doesn’t use regularization to fight against over-fitting. Instead, it employs tree pruning. Selecting the right hyper-parameters (tree depth and leaf size) also requires experimentation, e.g. doing cross-validation with a hyper-parameter matrix. For more information, please refer here.

 

2. Neural networks

a) cannot be used in ensemble

b) can be used for regression

c) can be used for classification

d) always output values will be between 0 and 1

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Ans : (b) and (c)

Answer: (b) can be used for regression and (c) can be used for classification Regression refers to predictive modeling problems that involve predicting a numeric value given an input. Classification refers to predictive modeling problems that involve predicting a class label or probability of class labels for a given input. Neural networks can be used for either regression or classification. Under regression model a single value is outputted which may be mapped to a set of real numbers meaning that only one output neuron is required. Under classification model an output neuron is required for each potentially class to which the pattern may belong. If the classes are unknown unsupervised neural network techniques such as self organizing maps should be used. For more information, please refer here.

 

3. Lasso can be interpreted as least-squares linear regression where

a) weights are regularized with the l₁ norm

b) the weights have a Gaussian prior

c) weights are regularized with the l₂ norm

d) the solution algorithm is simpler

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Ans : (a)

Answer: (a) weights are regularized with the l1 norm   Regularization is a technique to deal with over-fitting problem. Lasso regression Lasso regression is a regularization technique. This model uses shrinkage. Shrinkage is where data values are shrunk towards a central point as the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters). A sparse solution could avoid over-fitting. Lasso regression performs L1 regularization, which adds a penalty equal to the absolute value of the magnitude of coefficients. This type of regularization can result in sparse models with few coefficients; Some coefficients can become zero and eliminated from the model. Why l1 norm? By L1 regularization, you essentially make the vector smaller (sparse), as most of its components are useless (zeros), and at the same time, the remaining non-zero components are very “useful”.

 

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Related links:

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List the type of regularized regression

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What is regularized linear regression 

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Machine Learning Multiple Choice Questions and Answers 25

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, overfitting in non-parametric machine learning algorithms, decision tree, linear regression

Machine learning Quiz Questions - Set 25

 

1. Which of the following is a disadvantage of non-parametric machine learning algorithms?

a) Capable of fitting a large number of functional forms (Flexibility)

b) Very fast to learn (Speed)

c) More of a risk to overfit the training data (Overfitting)

d) They do not require much training data

Answer: (c) More of a risk to overfit the training data Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function. For example, decision trees are a nonparametric machine learning algorithm that is very flexible and is subject to overfitting training data. [For more, please refer Overfitting and Underfitting With Machine Learning Algorithms]

 

2. A decision tree has low training error and a large test error. What is the possible problem?

a) Decision tree is too shallow

b) Learning rate too high

c) There is too much training data

d) Decision tree is overfitting

Answer: (d) Decision tree is overfitting Overfitting causes low training error. Overfitting means that the model predicts the (training) data too well. It is too good to be true. If the new data point comes in, the prediction may be wrong. Pruning can help in reducing the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.

 

3. Suppose we have a regularized linear regression model. What is the effect of increasing λ on bias and variance?

a) Increases bias, increases variance

b) Increases bias, decreases variance

c) Decreases bias, increases variance

d) Decreases bias, decreases variance

Answer: (b) Increases bias, decreases variance Increasing λ increases bias and decreases variance Regularized regression It is a type of regression where the coefficient estimates are constrained to zero. The magnitude (size) of coefficients, as well as the magnitude of the error term are penalized. Complex models are discouraged, primarily to avoid overfitting. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. [For more refer here – regularized regression, ] and [Refer here - regularization ] Type of regularized regression Ridge regression (L2 regularization) Lasso regression (L1 regularization)

 

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Related links:

• Machine learning MCQ home page

• Machine learning TRUE / FALSE questions home page

• Computer science engineering quiz questions and answers page

• Data Warehousing and Data Mining MCQ with answers home
  

List the type of regularized regression

Multiple choice quiz questions in machine learning

What is regularized linear regression 

low training error vs large test error in decision tree

What is the disadvantage of non-parametric machine learning algorithms